import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn

from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging


__a    :	Dict         =    logging.get_logger(__name__)




class   _UpperCamelCase (		_UpperCAmelCase					):


			"""simple docstring"""






			def __init__(  self ,  lowerCAmelCase__		)	->   Tuple:

							'''simple docstring'''


							super().__init__()
							__lowercase					     =				nn.ModuleList(lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ = None ,  lowerCAmelCase__ = None ,  lowerCAmelCase__ = None ,  lowerCAmelCase__ = None ,  lowerCAmelCase__ = False ,  lowerCAmelCase__ = True ,  )	->   Union[ControlNetOutput, Tuple]:

							'''simple docstring'''


							for i, (image, scale, controlnet) in enumerate(zip(lowerCAmelCase__ ,  lowerCAmelCase__ ,  self.nets		)		):
											__lowercase     ,	__lowercase					     =				controlnet(
											    lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  )

											# merge samples
											if i == 0:
															__lowercase     ,	__lowercase					     =				down_samples, mid_sample
											else:
															__lowercase					     =				[
															    samples_prev + samples_curr
															    for samples_prev, samples_curr in zip(lowerCAmelCase__ ,  lowerCAmelCase__		)
															]
															mid_block_res_sample += mid_sample

							return down_block_res_samples, mid_block_res_sample





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ = True ,  lowerCAmelCase__ = None ,  lowerCAmelCase__ = False ,  lowerCAmelCase__ = None ,  )	->   Optional[int]:

							'''simple docstring'''


							__lowercase					     =				0
							__lowercase					     =				save_directory
							for controlnet in self.nets:
											controlnet.save_pretrained(
											    lowerCAmelCase__ ,  is_main_process=lowerCAmelCase__ ,  save_function=lowerCAmelCase__ ,  safe_serialization=lowerCAmelCase__ ,  variant=lowerCAmelCase__ ,  )

											idx += 1
											__lowercase					     =				model_path_to_save + F"_{idx}"





			@classmethod
			def 			_SCREAMING_SNAKE_CASE     (  cls ,  lowerCAmelCase__ ,  **lowerCAmelCase__		)	->   Any:

							'''simple docstring'''


							__lowercase					     =				0
							__lowercase					     =				[]

							# load controlnet and append to list until no controlnet directory exists anymore
							# first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
							# second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
							__lowercase					     =				pretrained_model_path
							while os.path.isdir(lowerCAmelCase__		):
											__lowercase					     =				ControlNetModel.from_pretrained(lowerCAmelCase__ ,  **lowerCAmelCase__		)
											controlnets.append(lowerCAmelCase__		)

											idx += 1
											__lowercase					     =				pretrained_model_path + F"_{idx}"

							logger.info(F"{len(lowerCAmelCase__		)} controlnets loaded from {pretrained_model_path}."		)

							if len(lowerCAmelCase__		) == 0:
											raise ValueError(
											    F"No ControlNets found under {os.path.dirname(lowerCAmelCase__		)}. Expected at least {pretrained_model_path + '_0'}."		)

							return cls(lowerCAmelCase__		)
from ...configuration_utils import PretrainedConfig
from ...utils import logging


__a    :	int         =    logging.get_logger(__name__)

__a    :	Optional[int]         =    {
    """google/pegasus-large""": """https://huggingface.co/google/pegasus-large/resolve/main/config.json""",
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}




class   _UpperCamelCase (		_UpperCAmelCase					):


			"""simple docstring"""






			__a    :					Any     					=		'''pegasus'''
			__a    :					Optional[Any]     					=		['''past_key_values''']
			__a    :					Tuple     					=		{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}





			def __init__(  self ,  lowerCAmelCase__=5_02_65 ,  lowerCAmelCase__=10_24 ,  lowerCAmelCase__=12 ,  lowerCAmelCase__=40_96 ,  lowerCAmelCase__=16 ,  lowerCAmelCase__=12 ,  lowerCAmelCase__=40_96 ,  lowerCAmelCase__=16 ,  lowerCAmelCase__=0.0 ,  lowerCAmelCase__=0.0 ,  lowerCAmelCase__=True ,  lowerCAmelCase__=True ,  lowerCAmelCase__="gelu" ,  lowerCAmelCase__=10_24 ,  lowerCAmelCase__=0.1 ,  lowerCAmelCase__=0.0 ,  lowerCAmelCase__=0.0 ,  lowerCAmelCase__=0.02 ,  lowerCAmelCase__=0 ,  lowerCAmelCase__=False ,  lowerCAmelCase__=0 ,  lowerCAmelCase__=1 ,  lowerCAmelCase__=1 ,  **lowerCAmelCase__ ,  )	->   Dict:

							'''simple docstring'''


							__lowercase					     =				vocab_size
							__lowercase					     =				max_position_embeddings
							__lowercase					     =				d_model
							__lowercase					     =				encoder_ffn_dim
							__lowercase					     =				encoder_layers
							__lowercase					     =				encoder_attention_heads
							__lowercase					     =				decoder_ffn_dim
							__lowercase					     =				decoder_layers
							__lowercase					     =				decoder_attention_heads
							__lowercase					     =				dropout
							__lowercase					     =				attention_dropout
							__lowercase					     =				activation_dropout
							__lowercase					     =				activation_function
							__lowercase					     =				init_std
							__lowercase					     =				encoder_layerdrop
							__lowercase					     =				decoder_layerdrop
							__lowercase					     =				use_cache
							__lowercase					     =				encoder_layers
							__lowercase					     =				scale_embedding  # scale factor will be sqrt(d_model) if True
							super().__init__(
							    pad_token_id=lowerCAmelCase__ ,  eos_token_id=lowerCAmelCase__ ,  is_encoder_decoder=lowerCAmelCase__ ,  decoder_start_token_id=lowerCAmelCase__ ,  forced_eos_token_id=lowerCAmelCase__ ,  **lowerCAmelCase__ ,  )





			@property
			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   int:

							'''simple docstring'''


							return self.encoder_attention_heads





			@property
			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   int:

							'''simple docstring'''


							return self.d_model